Rapid Prototyping for AI Projects
The forward-deployed engineer's core skill: turn a vague client ask into a scoped, working prototype — fast — without building the wrong thing. Frame an ambiguous problem, choose the right approach (RAG vs. agent vs. classic ML vs. workflow vs. no-AI) and the right model (quality vs. cost vs. latency, with routing), cut scope to the smallest convincing build, and demo a go/no-go. The front of the FDE journey; a prototype is a question, not a product.
"'Our support team is drowning. Can AI help?' — nine words to a scoped, running prototype and a go/no-go"
8 Interactive Sessions
Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.
- 1
A prototype is a question, not a product
Turn the instinct to start coding into the discipline to frame first — because the worst thing an FDE can do with an ambiguous ask is build the wrong thing beautifully.
- 2
Frame it, then write down what would kill it
Turn the instinct to frame first into three repeatable moves — map the as-is process, map persona impact, and agree the number that would make you say no — so the real problem and the bar for answering it are both settled before any code exists.
- 3
The backend is a database, not a vector store
Look at the data before choosing a technology — because the shape of the data decides the tool, and one query can tell you the scope was wrong while it is still free to change.
- 4
The help centre is prose — now you need retrieval
Watch keyword matching fail twice on real tickets, fix it with embeddings, and then build the one thing that makes any of those numbers trustworthy — a labelled answer key.
- 5
SQL, retrieval, or both
Build the decision that makes two working parts into one useful system — and understand why it returns more than any model upgrade would.
- 6
LangGraph — the pieces become a system
Turn five working functions into one system with a shape — so the routing is visible, the human step is countable, and a stakeholder can follow it without reading code.
- 7
A surface someone else can use
Put the system in front of a non-technical stakeholder — and choose the surface on what it has to prove, not on what would be right in production.
- 8
Demo, decide, hand over
Turn a working prototype into a decision — and be as willing to recommend no as yes, because a fast, well-evidenced no is the most valuable thing an FDE produces.
Production patterns you'll master
Synthetic data included
- Ambiguous client brief
- Discovery question bank
- Approach-selection cases
- Model comparison matrix
- Scoped prototype scaffold
What you walk away with
Shareable portfolio
A public URL showing your module timeline, patterns mastered, and completion status.
All the code
Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.
Module walkthrough
Each module documented with deliverables and the production pattern you implemented.
Ready to build your rapid prototyping for ai projects?
First course free. $20 per course after that.